1. The Field of the Invention
The present method relates to determining radiation exposure, more specifically, to a method for analyzing chromosome damage in cells that have been exposed to ionizing radiation by analyzing images of their centromere.
2. Background Art
Chromosome testing of blood through microscopy and manual scoring are presently the gold standards for evaluating human radiation exposures. (Blakely W F, et al. Health Phys. 89:494-504, 2005). However, these techniques presented problems with obtaining an accurate throughput. In these well known methods, blood cell cultures are first analyzed by Giemsa or Bromodeoxyuridine/Hoechst 33258/Giemsa (Fluorescence plus Giemsa; FPG). This results in the staining of the chromosome during its metaphase stage. The stain spreads to determine the frequencies of the two most common forms of chromosome aberrations, dicentric chromosomes (DCC), and acentric chromosomes. The absorbed dose is subsequently determined by comparison with cellular standards exposed to calibrated radiation doses. This traditional method of recognizing dicentric chromosomes (DCC) currently involves microscopy and manual scoring by a cytogeneticist. However, these manual, non-automatic techniques severely limit the throughput of the DCC assay.
The accurate recognition of DCC must also be counterbalanced with the need to analyze thousands of metaphase images efficiently in the event of major radiation incidents or cases in which individuals are accidentally overexposed to medical or industrial radiation. Thus, automating this cytogenetic analysis has long been sought because of its impact on turnaround times following such unfortunate events.
There are commercially available semi-automatic cytogenetic systems. These systems basically depend on four features: (a) metaphase cell finding, (b) metaphase quality ranking, (c) relocation of metaphases at high magnification and (d) ability of systems to find analyzable metaphases otherwise overlooked by a cytogeneticist (Korthof G & A D Carothers. Clin Genet. 40(6):441-51, 1991).
A recent analysis with a commercial automated system produced rapidly collected scorable metaphase images. Unfortunately, this system showed 50% misclassification of the DCC (Vaurijoux et al. Radiation Research 171: 541-548, 2009). The reason for the high false detection was a faulty algorithm. The system used an algorithm that scored normal chromosomes as DCC when low radiation dose (<2 Gy) were present, especially in cells with underspread or overlapped chromosomes (Schunck et al. Cytogenet Genome Res 104:383-389, 2004). Thus, there is a need for an approach which reduces false positive detection of DCCs arising from this category of metaphase cells.
Another problem with the present methods of analyzing chromosomes is that an imprecise method of image segmentation is used to partition the chromosome's metaphase image into many non-overlapping regions that correspond to individual chromosome objects. This causes error because chromosomes demonstrate high variability in shape on microscope slides mainly due to stages of cell cycle, length of mitotic arrest, slide preparation and banding patterns. This presents a significant challenge for automated segmentation, as well as for extracting the centerline of a chromosome, requiring expert input (Moradi M et al., “Automatic locating the centromere on human chromosome pictures,” in 16th IEEE Symposium on Computer-Based Medical Systems, 2003; Moradi M and S K Saterandan, Pattern Recognition Letters, 27:19-28, 2006).
Most existing approaches for chromosome segmentation rely on a form of pixel value thresholding (Graham J et al., Chromosome Analysis Protocols, 29:141-185, 1994). Thresholding is a point processing method that performs well on images consisting of objects well defined by pixel intensities that contrast clearly against background levels. Chromosomal images possess this quality to some extent. Otsu's method (and variations of this method) assign pixels as either object or background regions based on a single value, and have been used for the initial stage of segmentation of both Giemsa and DAPI banded (4′,6-diamidino-2-phenylindole) chromosome images (Popescu M et al. Computers in Biology and Medicine, 29:61-82, 1999; Wolf G. et al., “A PC-based program for evaluation of comparative genomic hybridization (cgh) experiments (URL)”; Gajendran B and J. Rodriguez, in Intl Conf on Image Processing (ICIP), pp. 24-27, 2004; Canny J, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 8(6), 1986; Ji, L. Cytometry, 17:196-208, 1994). Global thresholding by pre-processing images with a median filter, followed by 4-connected component labeling removes noise in the binary image (Wang X et al, J Biomedical Informatics, 41:264-271, 2008; Wang X et al., J Biomedical Informatics, vol. 42: 22-31, 2009).
Furthermore, all of these methods are prone to effects of uneven illumination, which can segment spurious objects that are partially discontinuous. The thresholded objects may contain holes due to small variations in illuminance (especially in fluorescence microscopy). Using local or adaptive thresholding, images have been divided into manually specified tessellations of fixed size and thresholded based on Otsu's method (Enrico G et al., in Proc. 29th Intnl Conf of IEEE EMBS, pp. 23-26, 2007; Otsu, N A. IEEE Trans. on Systems, Man and Cybernetics, SMC-9 (1): 62-66. 1979). Thresholded images are still sensitive to quantization errors present in the digital image. Intensity fading is also evident, resulting in cropping at the boundaries of the chromosome object.
Parametric deformable modeling has also been used to improve chromosome segmentation. Gradient Vector Flow (GVF)-based active contours is a modeling approach that exhibits greater accuracy for these objects. This model addresses a main limitation in the traditional active contours (Kass M et al., Intnl J of Computer Vision, 1(4): 321-331, 1988) by drastically increasing the capture range of contours (Xu C and J L Prince, “Gradient vector flow: A new external force for snakes,” in Proc IEEE Comp Soc Conf on Computer Vision and Pattern Recognition, 1997).
The GVF snake model significantly improves chromosome segmentation compared to thresholding techniques (Britto P and G. Ravindran, Inform Tech Journal, 6 (1): 1-7, 2007; Li C et al, “Segmentation of edge preserving gradient vector flow: An approach towards automatically initializing and splitting of snakes,” in Proc IEEE Comp Soc Conf on Computer Vision and Pattern Recognition, 2005). Being a parametric active contour, the global minimum (of an objective function) is not guaranteed unless the control points are initialized in the vicinity of the desired contour. Otherwise, the contour could converge to an unwanted local minimum such as a chromosomal band (which has a strong intensity gradient), on a non-chromosomal structure or even on the contour of another chromosome.
The chromosome's centerline is an important feature that serves as a reference for numerous measurements. The centerline feature is essential for recognition of centromeres and ultimately, DCC. It can be used to obtain the total length of the chromosome, the centromere location and centromere index value, the coordinates of the telomeric regions, and the banding pattern of a chromosome, all which may identify and classify a particular chromosome. Medial Axis Transform (MAT) and morphological thinning are often used to locate the medial axes of chromosomes. The boundary of the segmented chromosome image can be smoothed by morphological closing (dilation followed by the erosion operators) before applying MAT to skeletonize and obtain the centerline (Wolf G. et al., “A PC-based program for evaluation of comparative genomic hybridization (cgh) experiments (URL)”).
Obtaining the chromosome's centerline was normally achieved by numerous methods that involved the mapping of the chromosome's skeleton. Spurious branches in a chromosome's skeleton occur and have to be corrected. Bifurcations in the skeleton towards the ends of the chromosome can be mitigated by deriving a line from the triangle formed by the two segments and the telomeric chromosome boundary (Moradi M and S K Saterandan, Pattern Recognition Letters, 27:19-28, 2006). This method fails if branches form at a distance from the telomere, which is common in bent chromosomes. Thinning procedures produce fewer spurious branches than skeletonization, however, the thinned result often has missing data at the chromosome ends (Lam L & S W Lee, IEEE Trans on Pattern Analysis & Machine Intelligence, 14:869-885, 1992; Jang B K and T C Roland, “Analysis of thinning algorithms using mathematical morphology,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 12(6), March 1990). Morphological thinning of binarized and median filtered chromosome images has been used to obtain the centerline (Gajendran B and J. Rodriguez, in Intl Conf on Image Processing (ICIP), pp. 24-27, 2004; Wang X et al., J Biomedical Informatics, vol. 42: 22-31, 2009; Wang X. et al. Comp. Meth. & Programs in BioMedicine, 89:33-42, 2008). However, these approaches may still lead to spurious branches as well as bifurcations near telomeres. In images with rough chromosome boundaries, the spurious branches are prominent regardless of the filtering method. Subsequent pruning of these branches is the major limitation of both MAT and thinning. Furthermore, both procedures derive a set of points in space (“join the dots”), rather than a parametric curve, which is more relevant information for automated chromosome analysis. The medial axis has also been localized in straight chromosomes without skeletonization or thinning. Chromosomes are rotated until vertically oriented and midpoints of the horizontal chromosome slices are connected to obtain a medial axis which is then smoothed (Piper, J et al. Cytometry, 16: 7-16. 1994). Another method locates dominant points of the chromosome to derive a centerline25. Both approaches are not reliable if chromosomes are highly bent or blurred.
Sampling of chromosomes using cross-sections at different inclinations and combined midpoints can also be used to obtain an approximate medial axis (Ritter G and G. Schreib, Pattern Recognition Journal, 4: 923-938, 2001). However, the drawback of this method is that it attempts to get a polygonal approximation of the medial axis, instead of the medial axis itself and poor results were obtained when the segmented boundaries had irregular shapes. There is a need for a method for drawing the centerline accurately, and which is robust for various chromosome morphologies.
The chromosome's centerline is essential for recognition of centromeres and ultimately, DCC. For the foregoing reasons, there is a need for an automated method to determining radiation exposure, more specifically, to a method for analyzing chromosome damage in cells that have been exposed to ionizing radiation by analyzing images of their centromere.